Objective: To compare the accuracy of two different artificial neural networks (ANNs) for the discrimination of benign and malignant gastric lesions using morphometric and textural data on the nucleus.
Study design: Three thousand cells from 30 cancer cases, 26 cases of gastritis and 64 cases of ulcer were selected as a training set, and an additional 10,300 cells from equal cases of cancer, gastritis and ulcer were used as a test set using two different neural net architectures: back propagation (BP) and learning vector quantizer (LVQ). Images of routinely processed gastric smears stained by the Papanicolaou technique were processed by a custom image analysis system.
Results: Application of the BP and three variations of the LVQ established correct classification of more than 97% of the benign cells and more than 95% of the malignant cells, obtaining an overall accuracy of more than 97%.
Conclusion: This study not only presents a comparative study of the abilities of ANNs but also indicates that the use of ANNs and image morphometry may offer useful information on the potential of malignancy of gastric cells.